Overview

Dataset statistics

Number of variables21
Number of observations36574
Missing cells0
Missing cells (%)0.0%
Duplicate rows58
Duplicate rows (%)0.2%
Total size in memory5.9 MiB
Average record size in memory168.0 B

Variable types

Categorical8
Numeric11
DateTime2

Warnings

Dataset has 58 (0.2%) duplicate rowsDuplicates
land_area is highly correlated with bathroomHigh correlation
living_room is highly correlated with bedroomHigh correlation
bedroom is highly correlated with living_room and 1 other fieldsHigh correlation
bathroom is highly correlated with land_area and 1 other fieldsHigh correlation
land_area is highly correlated with bedroom and 2 other fieldsHigh correlation
living_room is highly correlated with bedroom and 1 other fieldsHigh correlation
bedroom is highly correlated with land_area and 3 other fieldsHigh correlation
bathroom is highly correlated with land_area and 3 other fieldsHigh correlation
property_area is highly correlated with land_area and 2 other fieldsHigh correlation
built_year is highly correlated with price_per_sqmHigh correlation
price_per_sqm is highly correlated with built_yearHigh correlation
living_room is highly correlated with bedroom and 1 other fieldsHigh correlation
bedroom is highly correlated with living_room and 1 other fieldsHigh correlation
bathroom is highly correlated with living_room and 1 other fieldsHigh correlation
property_area is highly correlated with bedroom and 2 other fieldsHigh correlation
parking_type is highly correlated with is_managed and 1 other fieldsHigh correlation
price_per_sqm is highly correlated with land_areaHigh correlation
property_type is highly correlated with built_year and 3 other fieldsHigh correlation
built_year is highly correlated with property_type and 3 other fieldsHigh correlation
bedroom is highly correlated with property_area and 1 other fieldsHigh correlation
is_managed is highly correlated with parking_type and 4 other fieldsHigh correlation
total_floor is highly correlated with property_type and 3 other fieldsHigh correlation
trading_target is highly correlated with parking_type and 3 other fieldsHigh correlation
land_use is highly correlated with purposeHigh correlation
land_area is highly correlated with property_area and 1 other fieldsHigh correlation
purpose is highly correlated with property_type and 1 other fieldsHigh correlation
bathroom is highly correlated with property_area and 1 other fieldsHigh correlation
parking_type is highly correlated with trading_targetHigh correlation
is_managed is highly correlated with property_typeHigh correlation
trading_target is highly correlated with parking_typeHigh correlation
property_type is highly correlated with is_managedHigh correlation
parking_area is highly skewed (γ1 = 185.1225046) Skewed
living_room has 1549 (4.2%) zeros Zeros
bedroom has 1236 (3.4%) zeros Zeros
bathroom has 1108 (3.0%) zeros Zeros
parking_area has 27514 (75.2%) zeros Zeros
parking_price has 34744 (95.0%) zeros Zeros

Reproduction

Analysis started2021-07-18 05:23:49.509466
Analysis finished2021-07-18 05:24:19.920450
Duration30.41 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

purpose
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
住家用
33643 
住商用
 
2244
商業用
 
604
國民住宅
 
73
工業用
 
10

Length

Max length4
Median length3
Mean length3.001995953
Min length3

Characters and Unicode

Total characters109795
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row住家用
2nd row住家用
3rd row住家用
4th row住家用
5th row住家用

Common Values

ValueCountFrequency (%)
住家用33643
92.0%
住商用2244
 
6.1%
商業用604
 
1.7%
國民住宅73
 
0.2%
工業用10
 
< 0.1%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
住家用33643
92.0%
住商用2244
 
6.1%
商業用604
 
1.7%
國民住宅73
 
0.2%
工業用10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
36501
33.2%
35960
32.8%
33643
30.6%
2848
 
2.6%
614
 
0.6%
73
 
0.1%
73
 
0.1%
73
 
0.1%
10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter109795
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
36501
33.2%
35960
32.8%
33643
30.6%
2848
 
2.6%
614
 
0.6%
73
 
0.1%
73
 
0.1%
73
 
0.1%
10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Han109795
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
36501
33.2%
35960
32.8%
33643
30.6%
2848
 
2.6%
614
 
0.6%
73
 
0.1%
73
 
0.1%
73
 
0.1%
10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
CJK109795
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
36501
33.2%
35960
32.8%
33643
30.6%
2848
 
2.6%
614
 
0.6%
73
 
0.1%
73
 
0.1%
73
 
0.1%
10
 
< 0.1%

trading_target
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
房地(土地+建物)
19687 
房地(土地+建物)+車位
16887 

Length

Max length12
Median length9
Mean length10.38516432
Min length9

Characters and Unicode

Total characters379827
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row房地(土地+建物)+車位
2nd row房地(土地+建物)+車位
3rd row房地(土地+建物)+車位
4th row房地(土地+建物)+車位
5th row房地(土地+建物)

Common Values

ValueCountFrequency (%)
房地(土地+建物)19687
53.8%
房地(土地+建物)+車位16887
46.2%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
房地(土地+建物19687
53.8%
房地(土地+建物)+車位16887
46.2%

Most occurring characters

ValueCountFrequency (%)
73148
19.3%
+53461
14.1%
36574
9.6%
(36574
9.6%
36574
9.6%
36574
9.6%
36574
9.6%
)36574
9.6%
16887
 
4.4%
16887
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter253218
66.7%
Math Symbol53461
 
14.1%
Open Punctuation36574
 
9.6%
Close Punctuation36574
 
9.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
73148
28.9%
36574
14.4%
36574
14.4%
36574
14.4%
36574
14.4%
16887
 
6.7%
16887
 
6.7%
Open Punctuation
ValueCountFrequency (%)
(36574
100.0%
Math Symbol
ValueCountFrequency (%)
+53461
100.0%
Close Punctuation
ValueCountFrequency (%)
)36574
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han253218
66.7%
Common126609
33.3%

Most frequent character per script

Han
ValueCountFrequency (%)
73148
28.9%
36574
14.4%
36574
14.4%
36574
14.4%
36574
14.4%
16887
 
6.7%
16887
 
6.7%
Common
ValueCountFrequency (%)
+53461
42.2%
(36574
28.9%
)36574
28.9%

Most occurring blocks

ValueCountFrequency (%)
CJK253218
66.7%
ASCII126609
33.3%

Most frequent character per block

CJK
ValueCountFrequency (%)
73148
28.9%
36574
14.4%
36574
14.4%
36574
14.4%
36574
14.4%
16887
 
6.7%
16887
 
6.7%
ASCII
ValueCountFrequency (%)
+53461
42.2%
(36574
28.9%
)36574
28.9%

land_area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5232
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.97321841
Minimum0.01
Maximum2140.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum0.01
5-th percentile4.56
Q110.49
median16.67
Q326.21
95-th percentile81
Maximum2140.1
Range2140.09
Interquartile range (IQR)15.72

Descriptive statistics

Standard deviation32.36453646
Coefficient of variation (CV)1.295969784
Kurtosis1028.379018
Mean24.97321841
Median Absolute Deviation (MAD)7.21
Skewness18.6802785
Sum913370.49
Variance1047.46322
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6477
 
0.2%
26.976
 
0.2%
12.0371
 
0.2%
6869
 
0.2%
19.0369
 
0.2%
9.2262
 
0.2%
10.8262
 
0.2%
6262
 
0.2%
6.0961
 
0.2%
6560
 
0.2%
Other values (5222)35905
98.2%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.091
 
< 0.1%
0.352
 
< 0.1%
0.65
 
< 0.1%
0.739
< 0.1%
0.8516
< 0.1%
0.981
 
< 0.1%
1.081
 
< 0.1%
1.143
 
< 0.1%
1.181
 
< 0.1%
ValueCountFrequency (%)
2140.11
< 0.1%
21361
< 0.1%
7111
< 0.1%
704.52
< 0.1%
5781
< 0.1%
556.531
< 0.1%
536.681
< 0.1%
495.081
< 0.1%
4661
< 0.1%
454.21
< 0.1%

property_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
住宅大樓(11層含以上有電梯)
24979 
透天厝
4309 
華廈(10層含以下有電梯)
3383 
公寓(5樓含以下無電梯)
 
2277
套房(1房1廳1衛)
 
1626

Length

Max length15
Median length15
Mean length12.9921529
Min length3

Characters and Unicode

Total characters475175
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row住宅大樓(11層含以上有電梯)
2nd row華廈(10層含以下有電梯)
3rd row住宅大樓(11層含以上有電梯)
4th row住宅大樓(11層含以上有電梯)
5th row套房(1房1廳1衛)

Common Values

ValueCountFrequency (%)
住宅大樓(11層含以上有電梯)24979
68.3%
透天厝4309
 
11.8%
華廈(10層含以下有電梯)3383
 
9.2%
公寓(5樓含以下無電梯)2277
 
6.2%
套房(1房1廳1衛)1626
 
4.4%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
住宅大樓(11層含以上有電梯24979
68.3%
透天厝4309
 
11.8%
華廈(10層含以下有電梯3383
 
9.2%
公寓(5樓含以下無電梯2277
 
6.2%
套房(1房1廳1衛1626
 
4.4%

Most occurring characters

ValueCountFrequency (%)
158219
 
12.3%
(32265
 
6.8%
)32265
 
6.8%
30639
 
6.4%
30639
 
6.4%
30639
 
6.4%
30639
 
6.4%
28362
 
6.0%
28362
 
6.0%
27256
 
5.7%
Other values (19)145890
30.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter346766
73.0%
Decimal Number63879
 
13.4%
Open Punctuation32265
 
6.8%
Close Punctuation32265
 
6.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
30639
8.8%
30639
8.8%
30639
8.8%
30639
8.8%
28362
8.2%
28362
8.2%
27256
7.9%
24979
 
7.2%
24979
 
7.2%
24979
 
7.2%
Other values (14)65293
18.8%
Decimal Number
ValueCountFrequency (%)
158219
91.1%
03383
 
5.3%
52277
 
3.6%
Open Punctuation
ValueCountFrequency (%)
(32265
100.0%
Close Punctuation
ValueCountFrequency (%)
)32265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han346766
73.0%
Common128409
 
27.0%

Most frequent character per script

Han
ValueCountFrequency (%)
30639
8.8%
30639
8.8%
30639
8.8%
30639
8.8%
28362
8.2%
28362
8.2%
27256
7.9%
24979
 
7.2%
24979
 
7.2%
24979
 
7.2%
Other values (14)65293
18.8%
Common
ValueCountFrequency (%)
158219
45.3%
(32265
25.1%
)32265
25.1%
03383
 
2.6%
52277
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
CJK346766
73.0%
ASCII128409
 
27.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
30639
8.8%
30639
8.8%
30639
8.8%
30639
8.8%
28362
8.2%
28362
8.2%
27256
7.9%
24979
 
7.2%
24979
 
7.2%
24979
 
7.2%
Other values (14)65293
18.8%
ASCII
ValueCountFrequency (%)
158219
45.3%
(32265
25.1%
)32265
25.1%
03383
 
2.6%
52277
 
1.8%

living_room
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.740033904
Minimum0
Maximum22
Zeros1549
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5883102435
Coefficient of variation (CV)0.3381027474
Kurtosis44.99257646
Mean1.740033904
Median Absolute Deviation (MAD)0
Skewness0.4228411536
Sum63640
Variance0.3461089426
MonotonicityNot monotonic
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
227314
74.7%
17165
 
19.6%
01549
 
4.2%
3414
 
1.1%
4105
 
0.3%
511
 
< 0.1%
66
 
< 0.1%
75
 
< 0.1%
92
 
< 0.1%
221
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
01549
 
4.2%
17165
 
19.6%
227314
74.7%
3414
 
1.1%
4105
 
0.3%
511
 
< 0.1%
66
 
< 0.1%
75
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
221
 
< 0.1%
111
 
< 0.1%
92
 
< 0.1%
81
 
< 0.1%
75
 
< 0.1%
66
 
< 0.1%
511
 
< 0.1%
4105
 
0.3%
3414
 
1.1%
227314
74.7%

bedroom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.923333516
Minimum0
Maximum52
Zeros1236
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum52
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.311398753
Coefficient of variation (CV)0.4485970371
Kurtosis100.6856806
Mean2.923333516
Median Absolute Deviation (MAD)1
Skewness3.848160234
Sum106918
Variance1.719766691
MonotonicityNot monotonic
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
316638
45.5%
26940
19.0%
46630
 
18.1%
12673
 
7.3%
51626
 
4.4%
01236
 
3.4%
6557
 
1.5%
7142
 
0.4%
868
 
0.2%
918
 
< 0.1%
Other values (15)46
 
0.1%
ValueCountFrequency (%)
01236
 
3.4%
12673
 
7.3%
26940
19.0%
316638
45.5%
46630
 
18.1%
51626
 
4.4%
6557
 
1.5%
7142
 
0.4%
868
 
0.2%
918
 
< 0.1%
ValueCountFrequency (%)
521
 
< 0.1%
441
 
< 0.1%
291
 
< 0.1%
241
 
< 0.1%
233
< 0.1%
224
< 0.1%
211
 
< 0.1%
182
< 0.1%
171
 
< 0.1%
163
< 0.1%

bathroom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.908897031
Minimum0
Maximum50
Zeros1108
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum50
Range50
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.096129372
Coefficient of variation (CV)0.5742213198
Kurtosis164.2253516
Mean1.908897031
Median Absolute Deviation (MAD)0
Skewness6.686025097
Sum69816
Variance1.201499601
MonotonicityNot monotonic
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
220878
57.1%
19825
26.9%
32604
 
7.1%
41345
 
3.7%
01108
 
3.0%
5447
 
1.2%
6195
 
0.5%
787
 
0.2%
844
 
0.1%
98
 
< 0.1%
Other values (14)33
 
0.1%
ValueCountFrequency (%)
01108
 
3.0%
19825
26.9%
220878
57.1%
32604
 
7.1%
41345
 
3.7%
5447
 
1.2%
6195
 
0.5%
787
 
0.2%
844
 
0.1%
98
 
< 0.1%
ValueCountFrequency (%)
501
 
< 0.1%
301
 
< 0.1%
241
 
< 0.1%
236
< 0.1%
223
< 0.1%
201
 
< 0.1%
184
< 0.1%
163
< 0.1%
151
 
< 0.1%
143
< 0.1%

partition
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
35450 
 
1124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36574
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
35450
96.9%
1124
 
3.1%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
35450
96.9%
1124
 
3.1%

Most occurring characters

ValueCountFrequency (%)
35450
96.9%
1124
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter36574
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
35450
96.9%
1124
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Han36574
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
35450
96.9%
1124
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
CJK36574
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
35450
96.9%
1124
 
3.1%

property_area
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct15855
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.6978211
Minimum0.02
Maximum4119.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum0.02
5-th percentile44.87
Q189.56
median128.815
Q3171.81
95-th percentile317.67
Maximum4119.9
Range4119.88
Interquartile range (IQR)82.25

Descriptive statistics

Standard deviation89.26891038
Coefficient of variation (CV)0.6126990074
Kurtosis134.5069567
Mean145.6978211
Median Absolute Deviation (MAD)40.575
Skewness5.040330808
Sum5328752.11
Variance7968.938361
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.6567
 
0.2%
236.437
 
0.1%
73.0136
 
0.1%
180.5736
 
0.1%
226.3734
 
0.1%
73.5333
 
0.1%
74.4433
 
0.1%
379.1731
 
0.1%
80.0531
 
0.1%
49.4228
 
0.1%
Other values (15845)36208
99.0%
ValueCountFrequency (%)
0.021
< 0.1%
5.261
< 0.1%
6.271
< 0.1%
9.181
< 0.1%
9.261
< 0.1%
9.721
< 0.1%
11.821
< 0.1%
12.361
< 0.1%
12.751
< 0.1%
13.321
< 0.1%
ValueCountFrequency (%)
4119.91
< 0.1%
2687.981
< 0.1%
1542.211
< 0.1%
1367.531
< 0.1%
1251.491
< 0.1%
1165.641
< 0.1%
1159.471
< 0.1%
1080.771
< 0.1%
903.831
< 0.1%
873.251
< 0.1%

is_managed
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
27716 
8858 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36574
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
27716
75.8%
8858
 
24.2%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
27716
75.8%
8858
 
24.2%

Most occurring characters

ValueCountFrequency (%)
27716
75.8%
8858
 
24.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter36574
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
27716
75.8%
8858
 
24.2%

Most occurring scripts

ValueCountFrequency (%)
Han36574
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
27716
75.8%
8858
 
24.2%

Most occurring blocks

ValueCountFrequency (%)
CJK36574
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
27716
75.8%
8858
 
24.2%

total_floor
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.74487341
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum1
5-th percentile3
Q18
median14
Q315
95-th percentile27
Maximum85
Range84
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.807881711
Coefficient of variation (CV)0.568057739
Kurtosis15.84676228
Mean13.74487341
Median Absolute Deviation (MAD)3
Skewness2.196963602
Sum502705
Variance60.96301681
MonotonicityNot monotonic
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
158100
22.1%
144833
13.2%
52864
 
7.8%
122290
 
6.3%
131883
 
5.1%
41717
 
4.7%
71599
 
4.4%
21422
 
3.9%
241258
 
3.4%
161042
 
2.8%
Other values (29)9566
26.2%
ValueCountFrequency (%)
140
 
0.1%
21422
3.9%
3644
 
1.8%
41717
4.7%
52864
7.8%
6358
 
1.0%
71599
4.4%
8700
 
1.9%
9487
 
1.3%
10630
 
1.7%
ValueCountFrequency (%)
8587
 
0.2%
421
 
< 0.1%
4132
 
0.1%
3978
 
0.2%
373
 
< 0.1%
3648
 
0.1%
35301
0.8%
3426
 
0.1%
33155
0.4%
3055
 
0.2%

parking_area
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct1904
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.722022475
Minimum0
Maximum17098
Zeros27514
Zeros (%)75.2%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile32.85
Maximum17098
Range17098
Interquartile range (IQR)0

Descriptive statistics

Standard deviation90.34744507
Coefficient of variation (CV)13.44051517
Kurtosis35019.35907
Mean6.722022475
Median Absolute Deviation (MAD)0
Skewness185.1225046
Sum245851.25
Variance8162.660831
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
027514
75.2%
25.92134
 
0.4%
23.17104
 
0.3%
30.5100
 
0.3%
17.9895
 
0.3%
19.1492
 
0.3%
19.990
 
0.2%
19.7282
 
0.2%
17.8965
 
0.2%
55.760
 
0.2%
Other values (1894)8238
 
22.5%
ValueCountFrequency (%)
027514
75.2%
0.031
 
< 0.1%
0.152
 
< 0.1%
11
 
< 0.1%
1.281
 
< 0.1%
1.293
 
< 0.1%
2.241
 
< 0.1%
2.271
 
< 0.1%
2.61
 
< 0.1%
31
 
< 0.1%
ValueCountFrequency (%)
170981
< 0.1%
274.121
< 0.1%
150.31
< 0.1%
141.121
< 0.1%
125.641
< 0.1%
122.471
< 0.1%
111.41
< 0.1%
108.931
< 0.1%
108.261
< 0.1%
108.151
< 0.1%

parking_price
Real number (ℝ≥0)

ZEROS

Distinct166
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100366.6575
Minimum0
Maximum10000000
Zeros34744
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile46.15
Maximum10000000
Range10000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation532270.8544
Coefficient of variation (CV)5.30326373
Kurtosis61.15362116
Mean100366.6575
Median Absolute Deviation (MAD)0
Skewness7.102265919
Sum3670810131
Variance2.833122624 × 1011
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034744
95.0%
1000000116
 
0.3%
1200000109
 
0.3%
1500000108
 
0.3%
115000070
 
0.2%
80000068
 
0.2%
130000064
 
0.2%
160000060
 
0.2%
180000047
 
0.1%
105000042
 
0.1%
Other values (156)1146
 
3.1%
ValueCountFrequency (%)
034744
95.0%
11
 
< 0.1%
1301
 
< 0.1%
1500001
 
< 0.1%
2500001
 
< 0.1%
2700001
 
< 0.1%
2800001
 
< 0.1%
3000001
 
< 0.1%
4000002
 
< 0.1%
4500002
 
< 0.1%
ValueCountFrequency (%)
100000001
 
< 0.1%
88000001
 
< 0.1%
82000001
 
< 0.1%
76000001
 
< 0.1%
75000002
< 0.1%
74000001
 
< 0.1%
73500001
 
< 0.1%
72000003
< 0.1%
70500004
< 0.1%
70000003
< 0.1%

parking_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
19683 
坡道平面
12153 
坡道機械
3716 
升降機械
 
676
升降平面
 
109
Other values (3)
 
237

Length

Max length4
Median length1
Mean length2.379914693
Min length1

Characters and Unicode

Total characters87043
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row坡道平面
2nd row坡道平面
3rd row坡道平面
4th row坡道平面
5th row

Common Values

ValueCountFrequency (%)
19683
53.8%
坡道平面12153
33.2%
坡道機械3716
 
10.2%
升降機械676
 
1.8%
升降平面109
 
0.3%
其他102
 
0.3%
一樓平面100
 
0.3%
塔式車位35
 
0.1%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
19683
53.8%
坡道平面12153
33.2%
坡道機械3716
 
10.2%
升降機械676
 
1.8%
升降平面109
 
0.3%
其他102
 
0.3%
一樓平面100
 
0.3%
塔式車位35
 
0.1%

Most occurring characters

ValueCountFrequency (%)
19683
22.6%
15869
18.2%
15869
18.2%
12362
14.2%
12362
14.2%
4392
 
5.0%
4392
 
5.0%
785
 
0.9%
785
 
0.9%
102
 
0.1%
Other values (7)442
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter87043
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
19683
22.6%
15869
18.2%
15869
18.2%
12362
14.2%
12362
14.2%
4392
 
5.0%
4392
 
5.0%
785
 
0.9%
785
 
0.9%
102
 
0.1%
Other values (7)442
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Han87043
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
19683
22.6%
15869
18.2%
15869
18.2%
12362
14.2%
12362
14.2%
4392
 
5.0%
4392
 
5.0%
785
 
0.9%
785
 
0.9%
102
 
0.1%
Other values (7)442
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
CJK87043
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
19683
22.6%
15869
18.2%
15869
18.2%
12362
14.2%
12362
14.2%
4392
 
5.0%
4392
 
5.0%
785
 
0.9%
785
 
0.9%
102
 
0.1%
Other values (7)442
 
0.5%

land_use
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
24265 
11951 
其他
 
335
 
19
 
4

Length

Max length2
Median length1
Mean length1.009159512
Min length1

Characters and Unicode

Total characters36909
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
24265
66.3%
11951
32.7%
其他335
 
0.9%
19
 
0.1%
4
 
< 0.1%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
24265
66.3%
11951
32.7%
其他335
 
0.9%
19
 
0.1%
4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
24265
65.7%
11951
32.4%
335
 
0.9%
335
 
0.9%
19
 
0.1%
4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter36909
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
24265
65.7%
11951
32.4%
335
 
0.9%
335
 
0.9%
19
 
0.1%
4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Han36909
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
24265
65.7%
11951
32.4%
335
 
0.9%
335
 
0.9%
19
 
0.1%
4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
CJK36909
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
24265
65.7%
11951
32.4%
335
 
0.9%
335
 
0.9%
19
 
0.1%
4
 
< 0.1%

district
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
三民區
6757 
左營區
6218 
鳳山區
6088 
鼓山區
5883 
楠梓區
4666 
Other values (4)
6962 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters109722
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row新興區
2nd row鳳山區
3rd row鼓山區
4th row三民區
5th row三民區

Common Values

ValueCountFrequency (%)
三民區6757
18.5%
左營區6218
17.0%
鳳山區6088
16.6%
鼓山區5883
16.1%
楠梓區4666
12.8%
前鎮區2937
8.0%
苓雅區2264
 
6.2%
新興區916
 
2.5%
前金區845
 
2.3%

Length

Histogram of lengths of the category

Pie chart

ValueCountFrequency (%)
三民區6757
18.5%
左營區6218
17.0%
鳳山區6088
16.6%
鼓山區5883
16.1%
楠梓區4666
12.8%
前鎮區2937
8.0%
苓雅區2264
 
6.2%
新興區916
 
2.5%
前金區845
 
2.3%

Most occurring characters

ValueCountFrequency (%)
36574
33.3%
11971
 
10.9%
6757
 
6.2%
6757
 
6.2%
6218
 
5.7%
6218
 
5.7%
6088
 
5.5%
5883
 
5.4%
4666
 
4.3%
4666
 
4.3%
Other values (7)13924
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter109722
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
36574
33.3%
11971
 
10.9%
6757
 
6.2%
6757
 
6.2%
6218
 
5.7%
6218
 
5.7%
6088
 
5.5%
5883
 
5.4%
4666
 
4.3%
4666
 
4.3%
Other values (7)13924
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Han109722
100.0%

Most frequent character per script

Han
ValueCountFrequency (%)
36574
33.3%
11971
 
10.9%
6757
 
6.2%
6757
 
6.2%
6218
 
5.7%
6218
 
5.7%
6088
 
5.5%
5883
 
5.4%
4666
 
4.3%
4666
 
4.3%
Other values (7)13924
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
CJK109722
100.0%

Most frequent character per block

CJK
ValueCountFrequency (%)
36574
33.3%
11971
 
10.9%
6757
 
6.2%
6757
 
6.2%
6218
 
5.7%
6218
 
5.7%
6088
 
5.5%
5883
 
5.4%
4666
 
4.3%
4666
 
4.3%
Other values (7)13924
 
12.7%
Distinct2204
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
Minimum2012-01-04 00:00:00
Maximum2020-04-28 00:00:00
Histogram with fixed size bins (bins=50)

trading_year
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.756248
Minimum2012
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum2012
5-th percentile2014
Q12015
median2017
Q32018
95-th percentile2019
Maximum2020
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.700337546
Coefficient of variation (CV)0.0008431051339
Kurtosis-1.117668552
Mean2016.756248
Median Absolute Deviation (MAD)2
Skewness0.08912163036
Sum73760843
Variance2.891147771
MonotonicityNot monotonic
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20158143
22.3%
20196326
17.3%
20166246
17.1%
20175869
16.0%
20185851
16.0%
20142784
 
7.6%
20201271
 
3.5%
201373
 
0.2%
201211
 
< 0.1%
ValueCountFrequency (%)
201211
 
< 0.1%
201373
 
0.2%
20142784
 
7.6%
20158143
22.3%
20166246
17.1%
20175869
16.0%
20185851
16.0%
20196326
17.3%
20201271
 
3.5%
ValueCountFrequency (%)
20201271
 
3.5%
20196326
17.3%
20185851
16.0%
20175869
16.0%
20166246
17.1%
20158143
22.3%
20142784
 
7.6%
201373
 
0.2%
201211
 
< 0.1%
Distinct4312
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size285.9 KiB
Minimum1930-07-01 00:00:00
Maximum2020-03-17 00:00:00
Histogram with fixed size bins (bins=50)

built_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct74
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.858342
Minimum1930
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum1930
5-th percentile1978
Q11994
median1999
Q32009
95-th percentile2015
Maximum2020
Range90
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.45924896
Coefficient of variation (CV)0.005730030335
Kurtosis0.3938037068
Mean1999.858342
Median Absolute Deviation (MAD)7
Skewness-0.6853610139
Sum73142819
Variance131.3143868
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20144118
 
11.3%
19942859
 
7.8%
19952306
 
6.3%
19961909
 
5.2%
20061899
 
5.2%
20051630
 
4.5%
19931403
 
3.8%
19971389
 
3.8%
20151237
 
3.4%
19981218
 
3.3%
Other values (64)16606
45.4%
ValueCountFrequency (%)
19301
 
< 0.1%
19312
< 0.1%
19342
< 0.1%
19412
< 0.1%
19431
 
< 0.1%
19462
< 0.1%
19471
 
< 0.1%
19501
 
< 0.1%
19533
< 0.1%
19544
< 0.1%
ValueCountFrequency (%)
20203
 
< 0.1%
201969
 
0.2%
201884
 
0.2%
2017133
 
0.4%
2016371
 
1.0%
20151237
 
3.4%
20144118
11.3%
20131098
 
3.0%
2012956
 
2.6%
2011620
 
1.7%

price_per_sqm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct26688
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52348.82673
Minimum0
Maximum1048343
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size285.9 KiB

Quantile statistics

Minimum0
5-th percentile27548
Q138548.75
median48344.5
Q362482
95-th percentile85949.4
Maximum1048343
Range1048343
Interquartile range (IQR)23933.25

Descriptive statistics

Standard deviation22885.36321
Coefficient of variation (CV)0.4371705086
Kurtosis308.7067158
Mean52348.82673
Median Absolute Deviation (MAD)11476.5
Skewness9.440632576
Sum1914605989
Variance523739849.3
MonotonicityNot monotonic
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2041713
 
< 0.1%
339449
 
< 0.1%
357148
 
< 0.1%
434788
 
< 0.1%
366607
 
< 0.1%
423737
 
< 0.1%
438307
 
< 0.1%
476197
 
< 0.1%
452017
 
< 0.1%
363207
 
< 0.1%
Other values (26678)36494
99.8%
ValueCountFrequency (%)
04
< 0.1%
26771
 
< 0.1%
43521
 
< 0.1%
45151
 
< 0.1%
48041
 
< 0.1%
76391
 
< 0.1%
90161
 
< 0.1%
94001
 
< 0.1%
94611
 
< 0.1%
105831
 
< 0.1%
ValueCountFrequency (%)
10483431
< 0.1%
10120851
< 0.1%
8743811
< 0.1%
8519271
< 0.1%
7305441
< 0.1%
5422741
< 0.1%
4938271
< 0.1%
4447051
< 0.1%
3516171
< 0.1%
3448481
< 0.1%

Interactions

Correlations

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

purposetrading_targetland_areaproperty_typeliving_roombedroombathroompartitionproperty_areais_managedtotal_floorparking_areaparking_priceparking_typeland_usedistricttrading_datetrading_yearbuilt_datebuilt_yearprice_per_sqm
0住家用房地(土地+建物)+車位12.94住宅大樓(11層含以上有電梯)232205.371559.370坡道平面新興區2016-10-2720162016-06-17201676204.0
1住家用房地(土地+建物)+車位16.76華廈(10層含以下有電梯)232110.5380.000坡道平面鳳山區2019-10-1820191994-03-04199444603.0
2住家用房地(土地+建物)+車位24.86住宅大樓(11層含以上有電梯)232188.07130.000坡道平面鼓山區2016-04-1920161996-11-20199656681.0
3住家用房地(土地+建物)+車位15.26住宅大樓(11層含以上有電梯)242166.031223.080坡道平面三民區2014-10-2120141998-09-02199837644.0
4住家用房地(土地+建物)2.50套房(1房1廳1衛)11128.00140.000三民區2015-07-3020151999-10-14199933929.0
5住家用房地(土地+建物)98.00透天厝245103.2920.000前鎮區2019-09-1620191966-08-20196658089.0
6住家用房地(土地+建物)+車位17.15住宅大樓(11層含以上有電梯)242163.85150.000坡道機械鼓山區2017-07-0620172015-03-11201573238.0
7住家用房地(土地+建物)19.34住宅大樓(11層含以上有電梯)231106.44180.000左營區2015-03-2420151985-11-29198531755.0
8住家用房地(土地+建物)7.71住宅大樓(11層含以上有電梯)22184.22140.000鼓山區2018-12-1620182006-02-09200655806.0
9住家用房地(土地+建物)9.48住宅大樓(11層含以上有電梯)23291.17140.000左營區2016-04-0720162000-05-03200043874.0

Last rows

purposetrading_targetland_areaproperty_typeliving_roombedroombathroompartitionproperty_areais_managedtotal_floorparking_areaparking_priceparking_typeland_usedistricttrading_datetrading_yearbuilt_datebuilt_yearprice_per_sqm
36564住家用房地(土地+建物)4.90華廈(10層含以下有電梯)11136.1880.000左營區2015-01-0220151995-04-21199538695.0
36565住家用房地(土地+建物)7.49住宅大樓(11層含以上有電梯)22186.27150.000左營區2016-12-0720162008-01-03200862594.0
36566住家用房地(土地+建物)+車位28.97住宅大樓(11層含以上有電梯)244453.711548.824600000坡道平面苓雅區2017-10-0620172014-11-072014103485.0
36567住家用房地(土地+建物)4.39華廈(10層含以下有電梯)11151.0470.000三民區2019-02-2720191995-04-28199530035.0
36568住家用房地(土地+建物)8.55住宅大樓(11層含以上有電梯)23284.54150.000楠梓區2014-12-3020142014-05-09201445541.0
36569住家用房地(土地+建物)+車位19.27住宅大樓(11層含以上有電梯)242220.42150.000坡道平面前鎮區2019-12-1520192008-10-24200852536.0
36570住家用房地(土地+建物)+車位14.60住宅大樓(11層含以上有電梯)232154.81140.000坡道平面三民區2015-11-1620152001-06-15200151676.0
36571住家用房地(土地+建物)+車位20.56住宅大樓(11層含以上有電梯)232168.24150.000坡道平面三民區2015-02-1520151994-01-19199452306.0
36572住家用房地(土地+建物)20.91住宅大樓(11層含以上有電梯)132160.37160.000鼓山區2015-05-0820151995-01-26199554873.0
36573住家用房地(土地+建物)3.06套房(1房1廳1衛)11147.13210.000左營區2014-12-1820141995-10-19199533949.0

Duplicate rows

Most frequently occurring

purposetrading_targetland_areaproperty_typeliving_roombedroombathroompartitionproperty_areais_managedtotal_floorparking_areaparking_priceparking_typeland_usedistricttrading_datetrading_yearbuilt_datebuilt_yearprice_per_sqm# duplicates
29住家用房地(土地+建物)33.41住宅大樓(11層含以上有電梯)232173.82140.000左營區2015-12-1120152010-12-16201020417.06
14住家用房地(土地+建物)6.59華廈(10層含以下有電梯)11151.0470.000三民區2019-02-2720191995-04-28199531368.05
7住家用房地(土地+建物)4.39華廈(10層含以下有電梯)11151.0470.000三民區2019-02-2720191995-04-28199530310.03
26住家用房地(土地+建物)9.87套房(1房1廳1衛)11128.2750.000三民區2020-01-0620202006-03-22200655713.03
55住家用房地(土地+建物)+車位32.73住宅大樓(11層含以上有電梯)232263.731545.983800000坡道平面左營區2015-07-2720152014-07-11201474397.03
57商業用房地(土地+建物)14.00住宅大樓(11層含以上有電梯)000168.88120.000新興區2019-08-2220191995-12-14199550924.03
0住商用房地(土地+建物)7.17住宅大樓(11層含以上有電梯)233166.76350.000苓雅區2017-07-0620171997-04-21199744675.02
1住商用房地(土地+建物)76.00透天厝145218.5340.000楠梓區2017-06-0620172007-01-24200773216.02
2住家用房地(土地+建物)2.96住宅大樓(11層含以上有電梯)11136.67130.000新興區2015-06-2920151996-06-03199638860.02
3住家用房地(土地+建物)3.19套房(1房1廳1衛)01139.55120.000苓雅區2016-04-1120161994-10-05199425917.02